Support Vector Machines (SVM) as a tool has become one of
the most established techniques for analyzing functional magnetic
resonance imaging (fMRI) data in recent years. The ability
to deal with very high dimensions in the feature space as well
as it’s robustness have played a crucial role in promoting SVM’s
popularity among scientists in the field of neuroscience and related
research. These data were acquired during an experiment
conducted by the Max Planck Institue where 22 subjects were
given an investment decision task with changing levels of uncertainty.
Recent literature suggests that a lot of information about
individual differences in decision making lies in the variability of
the blood-oxygen-level dependent (BOLD) fMRI signals. Given
the computed variability of the BOLD level following the stimuli
I train an SVM to classify the subjects with respect to their risk
attitude. By reducing the dimensions of the input to the areas of
the brain previously ascertained as relevant for decision making
under uncertainty I decrease the computation time without using
time intensive dimension reduction techniques. I then compare
my results with the results and technique presented by Mohr
and H¨ardle et al. (2010).